(An important announcement: 01 Jan 2026: The website of the Journal of Modern Technology has been moved to a new domain. The publisher, journal ISSN, and Crossref DOI prefix remain unchanged. All accepted manuscripts will be published on the new website from January 2026 onward. Old Website: https://review.journal-of-modern-technology.com/index.php/jmt/index New website: https://journal.krrishpub.com/index.php/jmt/index Contact of Journal of Modern Technology: editor.jmt@krrishpub.com Contact of Publisher (Krrish Scientific Publications Pvt. Ltd.): support@krrishpub.com ) Journal of Modern Technology is an engineering international journal and aims to serve as a leading platform for the dissemination of innovative research and advancements in the modern technology contributing from Computer Science Engineering, Artificial Intelligence and Machine Learning, Electronics and Communication Engineering, Electrical Engineering, Green Energy Engineering, and aligned engineering disciplines. Format of Publication: Online Language of Publication: English Subject: Engineering Online ISSN: 3049-3102 Major Scope: Computer Science Engineering, Artificial Intelligence and Machine Learning, Electronics and Communication Engineering, Electrical Engineering, Green Energy Engineering, and aligned engineering disciplines. Detailed Scope: Computer Science Engineering : Network Security and Intrusion Detection Systems, Cyber-Physical Systems and IoT Security, Human-Centric Computing and User Experience Design, 5G/6G Networks and Beyond, Distributed Ledger and Peer-to-Peer Systems, Quantum Computing Algorithms and Applications, Usability Engineering and Interaction Design, Application design & development, Ethical and Social Implications of Computing, E-Learning Technologies and Intelligent Tutoring Systems, etc. Artificial Intelligence (AI) and Machine Learning (ML): Artificial Intelligence and Machine Learning for various complex analysis, Explainable AI (XAI) and Interpretability, Machine Learning and Deep Learning Architectures for various Applications, Image Segmentation and Reconstruction, AI and ML for Medical Imaging and Diagnostics, AI for Real-Time and Streaming Data Analysis, AI for Agriculture, Smart Cities, Homes, and Grids, Social and Economic Impacts of AI Deployment, Applications of AI and ML in Finance, Marketing, and Customer Retention, Big data analysis, Data Mining and Knowledge Discovery. Electronics and Communication Engineering: Design and Fabrication of Semiconductor Devices, Wearable Electronics and Smart Devices, VLSI Design and Nanoelectronics, Next-Generation Network Architectures and Protocols, Embedded Systems, Signal Processing and Applications, Advanced Communication Technologies, Optoelectronics and Photonics, Interdisciplinary Research in Electronics and Communication, etc. Electrical Engineering: Advanced Power Generation Technologies, Intelligent Monitoring, Protection, and Control of Grids, Electric Motors and Generators, Electric Drives, Power Electronics for Electric Vehicles and Grid Integration, Integration of Renewables in Smart Grids and Microgrids, Advanced Energy Storage Technologies, Energy Efficiency and Demand-Side Management, Electrification of Public Transport Systems, Real-Time Simulation and Digital Twin Technologies, Interdisciplinary Research in Power and Energy, etc. Green Energy Engineering: Renewable Energy Sources, Green Energy Technologies, Hybrid Energy Storage Solutions for Renewable Integration, Design and Optimization of Energy Conversion Devices, Energy Harvesting and Waste-to-Energy Solutions, Renewable Energy Solutions for Climate Change Mitigation, Policies and Regulatory Frameworks for Sustainable Energy, Applications of Renewable Energy in Industry and Agriculture, etc.
Krrish Scientific Publications Private Limited
Journal of Modern Technology (JMT) Journal operates under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This allows others distribute, remix, tweak, and build upon the work, even commercially, as long as they credit the authors for the original creation. All authors publishing in JMT Journal accept these as the terms of publication.
Comfort Level and Increased Utilization in Smart Buildings by Promoting Sustainability: A New Hybrid Genetic and Bat Algorithms
The challenge of effectively managing energy consumption in smart buildings has become increasingly significant in recent years. Both macroeconomic and microeconomic frameworks stand to benefit from efficient energy management strategies. Additionally, it is essential to ensure that tenants in smart buildings experience acceptable levels of comfort. By utilizing optimization algorithms, we can minimize energy consumption while maximizing user convenience. In this paper, we propose an agent-based optimization method grounded in a multi-layered architectural framework and its architecture comprises an intelligent agent that communicates with one another within a three-tier network system. Our research specifically focuses on reducing energy consumption costs and peak demand rates while simultaneously enhancing user comfort to the highest possible standard. However, this optimization challenge is notably complex due to the vast array of electrical devices and their varying functionalities. To address this complexity, we propose a hybrid optimization approach that leverages both Genetic Algorithms (GA) and Bat algorithms and evaluated the performance of our method using specific objective functions and drawn comparisons with recent studies on Smart Home and CU-BEM) datasets. The switch layer is responsible for monitoring user preferences and comfort levels. The coordination layer features a coordinating agent tasked with determining the optimal scheduling of electrical appliances to achieve cost reductions in electricity consumption while maximizing user comfort. Finally, the execution layer consists of performing agents directly managing device operations. This comprehensive approach seeks to create a more sustainable energy management system in smart buildings, contributing to both economic efficiency and enhanced tenant satisfaction.
Chulalongkorn university- building energy management system (CU-BEMS)
Copyright (c) 2025 Akram Aghanasab, Paniz Ghasem, Parsa Roghanaian, Maria Dzikuć, Yang Luo (Author) https://creativecommons.org/licenses/by-nc/4.0
Re-Evaluating the Functionalist Approach of Urban Management towards Sustainable Architecture and Urban Layout with Emphasis on the Role of Digital Technologies
Urban management’s persistent reliance on purely functionalist approaches often sidelines aesthetics and social vitality, particularly in sustainable architecture and urban layout. This tension between sociologists prioritizing citizen-centered social functions and architects championing environmental, aesthetic, and symbolic roles undermines holistic urban development. Focusing on Tehran’s District 6, this research interrogates how digital technologies can reconcile these perspectives to foster spaces that are efficient, sustainable, and culturally resonant. Through critical literature review and qualitative case analysis, we reveal how decades of functionalist management eroded District 6’s visual coherence, social equity, and historical identity. The area’s haphazard development, driven by ad-hoc construction and weak oversight, exemplifies the costs of neglecting integrated design. Yet, our findings propose a transformative pathway: strategically embedding digital tools (e.g., GIS mapping, AI-assisted restoration, AR/VR placemaking, and participatory platforms) within sustainable architecture principles can revitalize urban fabric. We demonstrate that digital integration enables more than technical efficiency-it re-centers human experience. Interactive public spaces, digitally enhanced heritage conservation, and real-time civic co-design tools strengthen belonging while optimizing resource use. However, success hinges on transcending institutional silos and empowering communities. Ultimately, this study argues for an aesthetic-functional hybrid model where digital technology mediates between sociological needs and architectural innovation. For cities like Tehran, this approach isn’t optional; it’s vital for crafting resilient, identity-rich neighborhoods that honor both people and place.
Copyright (c) 2025 Mohammad Karimi, Farid Damirchi (Author) https://creativecommons.org/licenses/by-nc/4.0
Smart Detection of Low Engagement in Students using Artificial Intelligence and Behavioral Data
This work aims to develop a basic artificial intelligence program that can identify the class's most indolent student. The purpose of the program is to examine specific behaviors that demonstrate a lack of effort. These include missing a lot of classes, arriving late, especially by more than twenty minutes, turning in assignments late, and receiving poor grades. The program evaluates each of these factors for each student and determines which ones fit all the criteria for being lazy. We start by entering each student's information. Their name, number of absences, tardiness on various days, final grade, and whether or not they turned in their work on time are all included in this. This makes it easier to evaluate each student's performance in relation to the class average. It determines whether the student meets each condition using straightforward checks, also known as Boolean logic, such as "true" or "false." A student is labeled lazy if they have missed more than four classes, been late more than twenty minutes at least once, failed to turn in assignments on time, and received a grade below the class average. The program creates a list of the lazy students after evaluating every one of them. It selects the student with the lowest score from this list. The term "laziest" is then applied to that student. The program indicates that no lazy student was found if no student satisfies every requirement. This prevents students from being unfairly judged if they only missed one or two things.
Copyright (c) 2025 Ortencia Laci, Keti Dervishi, Klea Vreto (Author) https://creativecommons.org/licenses/by-nc/4.0
Optimizing Data Retrieval and Update Operations in PHP-MySQL Applications: A Review
One of the most employed technology stacks for developing interactive, database-driven web applications is PHP with MySQL. As systems grow larger, the need for efficient data retrieval and updating becomes more crucial to ensure optimal performance, scalability, and user satisfaction. This study discusses several approaches to deal with the main issues related to PHP and MySQL integration performance. The detailed analysis concentrated on prepared statements, batch and asynchronous operations, normalization, persistent connections, query optimization, indexing, and caching, and the use of sophisticated frameworks. Examples from real-world situations show how the methods can enhance performance. To help direct future development efforts, additional conceptual frameworks are also offered, along with best practices and possible hazards. This study discusses several approaches to deal with the main issues related to PHP and MySQL integration performance. The detailed analysis concentrated on prepared statements, batch and asynchronous operations, normalization, persistent connections, query optimization, indexing and caching, and the use of sophisticated frameworks.
Copyright (c) 2025 Zagraxha Olta, Brikena Morina, Egzona Haskuka (Author) https://creativecommons.org/licenses/by-nc/4.0
Impact of Hydrogen Enriched Air and Ethanol-Diesel Fuel Blends on Combustion, Performance and Emission behaviour of CI Engine
The rapid depletion of fossil fuels and harmful tile pipe emissions form internal combustion engines at an alarming rate make the finding for alternative fuels to power the engines. This article investigates the diesel engine capability by the combined effects of ethanol and diesel fuel blending and hydrogen enriched intake air on the performance, combustion, and emission characteristics of 4 - stroke diesel engine. These experiments were conducted using three different fuel configurations such as 20% ethanol-80% diesel blend (E20D80), neat diesel (D100), and ethanol diesel blend (E20D80), supplemented with hydrogen enriched air (E20D80+H26LPM). The hydrogen was supplied to engine at constant flow rate of 6 LPM through manifold along with air. Three distinct fuels were used in the engine, and its performance, emissions, and combustion characteristics were assessed under load conditions ranging from 0% to 100%. Hydrogen enrichment of the intake air improves the combustion efficiency of ethanol by accelerating flame speed and enhancing thermal conversion, offsetting ethanol’s lower calorific value. A peak in-cylinder pressure of approximately 69 bar was achieved, indicating more effective combustion. CO emissions decreased significantly under part-load conditions due to improved oxidation kinetics. Ethanol’s high latent heat of vaporization helped maintain NOx emissions at levels comparable to diesel 1023 ppm. However, a slight increase in smoke emissions was observed with hydrogen addition, possibly due to richer local equivalence ratios and altered combustion dynamics.
Devi Veera Venkata Satya Bhimeswar Reddy Saragada
Copyright (c) 2025 Uma Kiran Rama Sandeep Kadimisetti, Kadupu Rakesh Varma, Ganapathi Sundarapalli, Devi Veera Venkata Satya Bhimeswar Reddy Saragada, S S M Sitaramamurty Adduri (Author) https://creativecommons.org/licenses/by-nc/4.0
SmartBio: An AI-Enabled Smart Medical Device for Early Cancer Detection using Variational Autoencoders and Multimodal Sensor Integration
This research explores the capability of a generative AI model called Variational Autoencoder (VAE), leveraging device sensors such as breath acetone and sweat biomarkers to identify life-threatening diseases, such as cancer, diabetes, and heart disease at earlier stages and help address metabolic issues. These sensors are intended to be integrated into smart devices such as wearable fitness trackers or smartwatches. The sweat biomarker sensor collects data from perspiration, including lactate, glucose, cortisol, and sodium levels. The breath acetone sensor measures the concentration of acetone in exhaled breath a byproduct of fat metabolism that reflects metabolic state. Both sensors can help assess mitochondrial quality, a core parameter for predicting diseases like cancer, diabetes, and cardiovascular disorders. The work demonstrates the efficacy of the system, achieving a training accuracy of 92%, testing accuracy of 89%, and an anomaly detection rate of 90%, with a low false positive rate of 5%. A reconstruction error threshold of 0.1 was empirically determined to differentiate between normal and abnormal patterns. The system’s architecture built on Azure cloud and edge infrastructure supports secure data storage, low-latency inference, and personalized health recommendations via mobile interfaces. Overall, SmartBio offers a proactive and scalable solution for personalized metabolic health monitoring, paving the way for early intervention and lifestyle-driven disease prevention.
Copyright (c) 2025 Reetha Vadakke Kara (Author) https://creativecommons.org/licenses/by-nc/4.0
Hybrid RNN-GRU-LSTM Model for Accurate Detection of DDoS Attacks on IDS Dataset
Distributed Denial of Service (DDoS) attacks are a persistent threat to network security, capable of disrupting critical services. This study proposes a hybrid deep learning model that combines Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) networks to effectively detect DDoS attacks in network traffic. Each component of the hybrid model captures unique temporal dependencies—RNN for basic sequence patterns, GRU for efficient short-term memory, and LSTM for long-term memory retention. The model is evaluated using two standard Intrusion Detection System (IDS) datasets, CIC-DDoS2019 and UNSW-NB15, representing diverse attack scenarios. Preprocessing techniques, including feature selection, normalization, and class balancing with Synthetic Minority Over-sampling Technique (SMOTE), ensure high-quality input data. Experimental results demonstrate that the hybrid model outperforms standalone RNN, GRU, and LSTM models, achieving superior accuracy, precision, recall, and F1-score. Specifically, the hybrid model achieves 97.3% accuracy, 97.0% precision, 97.6% recall, and an AUC of 0.981 on the CIC-DDoS2019 dataset. These results underscore the model’s capability to detect complex DDoS patterns while maintaining low false positive rates. The proposed approach offers a scalable, adaptive, and robust solution for real-time intrusion detection in dynamic network environments, outperforming traditional methods.
Synthetic Minority Over-sampling Technique
Copyright (c) 2025 Arun kumar Soma (Author) https://creativecommons.org/licenses/by-nc/4.0
A Systematic Approach for Protective Relay Coordination and Transient Stability Examination in Energy Networks with Substantial DG Penetration
A transient stability examination is crucial in pinpointing the constraints of the electric power systems. On the other hand, the infiltration level of distribution generation influences the constraints in creating safeguards in the power system. Therefore, these two parameters should be regarded as key elements in the power system conservation of the grid. In this work, a transient stability examination of a power system, including DGs, is accomplished to evaluate the protective settings of overcurrent relays (OCRs). For this purpose, the transient stability of DGs is scrutinized under fault conditions at the sites, as well as different DG infiltration degrees. Then, the limitations of DGs in the protective harmonization of OCRs are considered. A novel systematic approach for calculating the modified time multiplier setting (TMS) of OCRs is recommended. The recommended approach maintains coordination among OCRs, and the instability of DG sources is also prevented. Modeling of the IEEE 33 bus system using the ETAP application is used to test the suggested protection scheme's efficacy.
Copyright (c) 2025 Milad Taheri, Mohammad Parhamfar, Alireza Hajarkesht, Alireza Soleimani , Aykut Fatih Güven (Author) https://creativecommons.org/licenses/by-nc/4.0
A Novel Software Introduction for Enhanced Low-Voltage Electrical Installation Design in Buildings: VOLTA
This study introduces a revolutionary version of software for designing and building electrical installations. This software aims to provide optimal solutions within the domain of electrical installations, particularly in building contexts and low-voltage networks. The designed program will be able to generate DXF files with riser and single-line diagrams derived from the designed installation. In addition, a PDF summary of the performed computations will be provided. The suggested software is a significant step forward in the execution of international regulations and adherence to electrical engineers' standards within engineering firms. Furthermore, improvements in the quality of electrical installation execution in buildings and a decrease in the occurrence of rework are expected. This software uses optimization techniques such as a genetic algorithm for load balancing, as well as a particular menu for constructing and calculating capacitor banks, energy labeling calculations, protection coordination, etc. This software was developed with csharp and connected to AutoCAD with .NET capability.
Copyright (c) 2025 Mohammad Parhamfar (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigation on Optimal Distance between Transmitter and Receiver Coils in Wireless Power Transfer for Dynamic Charging System
The usage of electric vehicles (EVs) has significantly increased because of the worldwide movement toward environmentally friendly transportation. Notwithstanding these advancements, creating a comfortable and effective infrastructure for charging EVs remains a significant obstacle to their broad adoption. To overcome this difficulty, wireless power transfer (WPT) technology—specifically, WPT-DCS—has shown promise. With Wireless Power Transfer (WPT)- Dynamic Charging System (DCS), physical connectors are no longer necessary, and EV batteries may be refilled while on the road. In this paper, the development of an optimal distance for wireless power transfer in a dynamic charging system for electric vehicles is proposed. Research that investigated square and circular spiral coils to solve the challenges of WPT-DCS implementation focused on the effects of spiral geometries' thickness as well as the magnetic flux distribution, coupling factor, the receiver (RX) efficiency, RX power, and mutual inductance between TX (transmitter) and RX. Determining the ideal distance between TX and RX and TX and TX is the main goal for dynamic EV charging systems. This paper also presents the hardware setup that replicates the dynamic charging environment for square and circular spiral coils. The concept of emf generation and its utilization for charging the battery while the vehicle is in motion is presented in this paper.
Copyright (c) 2025 Anusha Vadde, Praveen L. S, Purvi Kolhar, Kruthika B. S , Megha, Sindhu V (Author) https://creativecommons.org/licenses/by-nc/4.0
Leveraging Sentiment Analysis in the Digital Era: Uncovering Insights from Unstructured Data for Enhanced Customer Engagement
The Google Play Store is a dynamic marketplace hosting a vast array of mobile applications across various categories. Analyzing user ratings and sentiments is essential for developers, marketers, and researchers to evaluate app performance and enhance user satisfaction. This study employs deep learning techniques, specifically a Long Short-Term Memory (LSTM)-based model, to examine user reviews and identify sentiment patterns. By leveraging natural language processing (NLP) and machine learning, the research investigates correlations between user feedback, app features, and overall ratings. The model processes and classifies user sentiments, such as positive, neutral, or negative and provides insights into key factors influencing user perceptions. Additionally, this study explores how app quality, functionality, and user engagement impact consumer satisfaction. Through data-driven analysis, it highlights the primary drivers of positive and negative reviews, offering a comprehensive understanding of user expectations and industry trends.
Copyright (c) 2025 Venkata Sandeep Edara, S Rama Satyanarayana Reddy, G Neha Akshaya, O Lakshmi Koteswari, T Sreeja (Author) https://creativecommons.org/licenses/by-nc/4.0
Advances in Nano Metal Oxides and Nanotechnology for Lithium-Ion Batteries in EV: A Comprehensive Review
Metal oxides are highly useful in electronics and environmental cleanup because, when exposed to the right amount of energy, they can produce charge carriers. The increasing demand for energy storage devices and growing environmental concerns have heightened interest in metal oxides due to their fascinating properties. Because of their ability to improve battery performance—specifically, energy density, power density, and cycle life—nano metal oxides have attracted a lot of interest in the field of electric vehicle (EV) battery research. A thorough assessment of research covering different types of metal oxide nanoparticles, methodology, applications, and analytical techniques is provided. This review article also categorizes the importance of different types of nano metal oxides and Nano Technology that have been explored for use in EV batteries, its applications in various fields and about use of nano metal oxides Lithium-ion batteries are discussed.
Copyright (c) 2025 Sravanthi C L, Chandra Sekhar J N (Author) https://creativecommons.org/licenses/by-nc/4.0
Sustainable Strategies for Energy Management in Buildings and Electric Vehicle Charging
This study investigates the integration of electric vehicle (EV) charging stations into buildings as a sustainable strategy to reduce greenhouse gas emissions and promote clean energy adoption. While this initiative offers significant environmental advantages, it also introduces challenges such as high installation costs, the need for electrical infrastructure upgrades, and potential power quality concerns. To address these issues, the research utilizes Pareto frontier analysis and multi-objective optimization (MOO) techniques to identify optimal trade-offs among key objectives, including cost, energy efficiency, and system reliability. Three strategies (A, B, and C) are assessed, with Strategy C emerging as the most balanced and cost-effective option. The findings underscore the importance of prioritizing both economic viability and operational efficiency in decision-making processes. Overall, integrating EV charging stations into buildings demonstrates considerable potential as a step toward sustainability. However, a thorough evaluation of all influencing factors is critical to ensuring long-term success and alignment with global environmental goals.
Copyright (c) 2025 Seyed Hossein Taheri , Parsa Tayebati, Mohammad Parhamfar (Author) https://creativecommons.org/licenses/by-nc/4.0
Simulation of Hydrogen Gas Transportation through Pipeline Manifold Systems in Gas Gathering Facilities using CFD Software
This paper presents a computational fluid dynamics (CFD) study on the simulation of hydrogen gas transportation through a pipeline manifold system using SimFlow. The project aims to evaluate the flow characteristics of hydrogen gas, focusing on velocity and pressure profiles to identify potential inefficiencies and areas for optimization. Using a simulated 3D model of a pipeline manifold, the study analyzes the behavior of hydrogen flow through varying pipeline geometries and cross-sections. Key findings include high-velocity regions reaching 2.3 m/s near the pipeline exit and low-velocity zones around 0.59 m/s near the inlet branches. Pressure variations were also observed, with high-pressure zones up to 4.3 MPa at the inlets and low-pressure regions dropping to -1.4 MPa near the outlet, indicating significant pressure loss along the pipeline. These variations contribute to turbulence, recirculation, and potential material stress. Convergence of the simulation was achieved after 388 iterations, indicating numerical stability in the solution. This study provides valuable insights into optimizing hydrogen transport infrastructure, ensuring safe and efficient delivery in pipeline system.
Copyright (c) 2024 Campbell Omuboye, Chigozie Nweke-Eze (Author) https://creativecommons.org/licenses/by-nc/4.0
Emerging Applications of Artificial Intelligence in Edge Computing: A Comprehensive Review
Edge computing, coupled with Artificial Intelligence (AI), represents a paradigm shift in data processing, enabling real-time analytics and decision-making at the source. By distributing computation closer to the data origin, this integration addresses the critical challenges of latency, bandwidth, and privacy, which traditional cloud-centric systems face. AI algorithms deployed on Edge devices enable localized intelligence, facilitating transformative advancements in domains such as healthcare, smart cities, industrial automation, and autonomous systems. This review comprehensively examines the synergistic relationship between AI and Edge computing, highlighting key applications, challenges, and future research opportunities. Moreover, we emphasize the critical need for lightweight AI models, energy-efficient systems, and robust security measures to fully harness the potential of Edge AI in an increasingly connected world.
Copyright (c) 2024 Rama Krishna Innmuri, Yeswanth Dintakurthy, Ashutosh Vanteru , Arun Thotakuri (Author) https://creativecommons.org/licenses/by-nc/4.0
Enhanced Brain Tumor Detection from MRI Scans Using Frequency Domain Features and Hybrid Machine Learning Models
This research proposes a machine learning-based approach to enhance the accuracy of brain tumor detection by incorporating advanced feature extraction techniques. Texture and shape information, which are critical for precise tumor characterization, were extracted from Magnetic resonance imaging (MRI) scans using Gabor and Radon features. The dataset used consists of 3,160 brain tumor images, categorized into three types of brain tumors and one category representing no tumor. Four classifiers were employed for classification: Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and AdaBoost. The results demonstrate that the recognition accuracies for Radon, Gabor, and combined features vary across classifiers. KNN achieved the highest accuracy of 95.50% with Radon features, SVM attained 96.65% with Gabor features, and SVM reported the best overall accuracy of 98.75% with combined features.
Copyright (c) 2024 Gauri Kalnoor, Kiran Sai Dasari, Suma S, Nischita Waddenkery, B. Pragathi https://creativecommons.org/licenses/by-nc/4.0
Analysis on Waste Reduction Strategies for Retailers among their SCM distributional partners
The retail sector in the USA is currently facing persistent supply chain waste problems, which encompass inefficiencies, excessive inventory, and logistical obstacles. To tackle these problems, specific tactics are needed to decrease escalating expenditures, minimise ecological consequences, and restore diminished financial viability. This study used a qualitative research methodology, utilising secondary data sources, to investigate strategies for reducing supply chain waste in the US retail business. The paper classifies various forms of waste within the supply chain, examines the associated difficulties and reasons that contribute to waste production, and presents a streamlined framework tailored specifically for the retail industry in the United States. Current study emphasis on enhancing the operational efficiency and sustainability aspects of organisations by reducing the supply chain waste. Furthermore, the study will highlight the need of taking proactive actions to mitigate the supply chain waste in USA retail industry by encouraging environmental stewardship. This analysis provides a comprehensive analysis, sensible recommendations, and a reverse logistics model to help US retailers expand and compete with each other and with global players as well.
Copyright (c) 2024 Guna Sekhar Sajja, Mohan Kumar (Author) https://creativecommons.org/licenses/by-nc/4.0
Reducing Switch Count in a Self-Balancing Nine-Level Switched Capacitor Inverter: A Modified Approach
Renewable energy sources are becoming more and more popular as a way to minimize environmental damage while meeting the world’s expanding demand for electricity. However, particular components like DC-AC converters, trans- former/inductor absence operation, front-end DC-DC converters with high gain capacity, and high voltage at a selected level are needed for small-scale PV solar independent AC loads and for grid applications as well as. This study offers a step-up boost inverter with quadruple operation of 9-levels that solves the problem by using a switched capacitor approach with less complications. By utilizing the control scheme itself, the topology is intended to balance capacitor voltages without the requirement for sensors. A phase disposition- pulse width modulation (PD-PWM) control technique is employed in this topology. This work focuses on the switching operation modes of the anticipated inverter and presents experimentally confirmed simulation experiments carried out in MATLAB/Simulink. All things considered; this research presents a viable option for renewable energy systems that can assist in satisfying the demand for electricity while preserving the environment.
Copyright (c) 2024 Lois Anuale, Osasumwen J. T. Alimikhena, Olafuyi Ogunmuyiwa (Author) https://creativecommons.org/licenses/by-nc/4.0
A Short review on Optimal Allocation of Microgrid
A microgrid (MG) is a revolutionary concept in electricity generation. The Microgrid concept entails a collection of loads and micro-sources functioning as a unified controllable system that delivers both electricity and thermal energy to its surrounding locality. Limited knowledge exists regarding the behavior of microgrids as comprehensive systems. Several models exist that delineate the components of a Microgrid. This study intends to emphasize certain research endeavors in Microgrids. The efforts evaluated in this paper are designed to facilitate further advancements in microgrid technology. The long-term objective is to deliver an advanced analysis of microgrids to facilitate a comprehensive understanding of their behavior.
Copyright (c) 2024 Yamuna Pagidela, N Visali (Author) https://creativecommons.org/licenses/by-nc/4.0
A Case Study on Optimal Tilt Angle and Spacing for Rooftop Solar Photovoltaic System at Bhubaneswar
The effectiveness of photovoltaic (PV) surfaces in capturing solar energy is influenced by the angle at which sunlight strikes them. This angle varies based on the time of day, date, and geographical location of the PV array installation. To maximize irradiance collection, PV modules should ideally be positioned perpendicular to incoming solar rays. Suboptimal placement can result in diminished system performance, highlighting the importance of installing PV modules at an ideal tilt. Additionally, shadow effects can significantly impact PV array efficiency. This research examines key aspects of PV system installation, with a focus on array positioning in terms of tilt angle and row spacing to reduce horizontal shading. When installing PV arrays, determining the optimal tilt angle is essential for maximizing irradiance reception. Row spacing is calculated to eliminate inter-array shadows. The study presents calculations for optimal tilt angle and spacing, validated through a prototype 11.2 kWp solar photovoltaic system installed at SOA, Bhubaneswar, India. The research determines monthly and yearly optimal tilts, solar flux gains, and energy yields for this specific location. Findings show a maximum solar flux gain of 32.97% in December, with a Global Solar Radiation (GSR) of 4.72 kWh/m2/day at a 40° optimal tilt. The lowest solar flux gains are observed in July, August, and September. Tilt adjustments during summer months (May to August) increase shading, which contributes to cooling effects.
Optimal angle, Solar radiation, spacing; power generation
Copyright (c) 2024 Sonali Goel, Bibekananda Jena, Renu Sharma (Author) https://creativecommons.org/licenses/by-nc/4.0
Ensemble Machine Learning Approaches for Robust Classification of Maize Plant Leaf Diseases
Maize, a fundamental crop globally, is particularly susceptible to a range of leaf diseases, which can result in substantial yield reductions and economic challenges for agricultural producers. Prompt and precise identification of these diseases is critical to minimizing their adverse effects on food security. This study investigates the application of ensemble machine learning methodologies to improve the robustness and accuracy of maize leaf disease classification. For this proposed experiment, the standard dataset has been utilized, dataset contains 3857 images belonging to blight, Common rust, gray leaf spot, and healthy leafs. By using this dataset three kinds of features (Gray-level co-occurrence matrix (GLCM), Local Binary Pattern (LBP) and Gabor) were extracted. This proposed experiment was carried out in three categories i.e., Single, Double and Multiple combination of features. These extracted features are submitted to three machine learning algorithms, such as s (SVM), kNN, and NN. In single feature Gabor with NN Classifier has given 85.40% as highest accuracy, in the Bi-features Gabor with LBP using NN algorithm has record the 88.00% as an output result, at last in Tri-features (Gabor + GLCM+LB) SVM has raised as a highest recognition accuracy as 88.80%.
Copyright (c) 2024 Ebrahim E. Elsayed, Mohammed Raisan Hayal, Davron Aslonqulovich Juraev (Author) https://creativecommons.org/licenses/by-nc/4.0
Natural Gas Utilization: Simulation of Steam Methane Reforming and Decarbonization by Diethanolamine Absorption for Hydrogen Production in Nigeria
This study explores the integration of steam methane reforming (SMR) with diethanolamine (DEA) absorption for carbon capture, using CAPE-OPEN Flowsheet Environment (COFE) and Aspen Hysys for simulation, focusing on hydrogen production for energy use in Nigeria. The integration aims to optimize operational parameters, regenerate the DEA solvent, and produce hydrogen with minimal CO₂ content, offering a sustainable solution for hydrogen production. Simulation results show that the process significantly enhances hydrogen yield, increasing its concentration from 50.00 mol% in syngas to 60.13 mol% in sweet gas, while reducing CO₂ content from 7.10 mol% to 0.06 mol%. The lean DEA solvent was successfully regenerated, achieving 100 mol% purity after stripping. The study highlights the environmental and economic benefits of this approach, particularly in addressing the impact of natural gas flaring and promoting hydrogen as a cleaner fuel in Nigeria. It provides policy recommendations to support the adoption of SMR-DEA technology, emphasizing its feasibility and alignment with global sustainability goals, positioning Nigeria for improved energy efficiency and a reduced carbon footprint.
Strengthening Resilience: A Brief Review of Cybersecurity Challenges in IoT-Driven Smart Grids
While efficiency and real-time monitoring have significantly improved with the integration of Internet of Things (IoT) devices into smart grids (SGs), new cybersecurity vulnerabilities have also been brought about by this process. This study examines the security risks associated with IoT-enabled SGs, emphasizing the possibility of cyberattacks that could interfere with grid operations or compromise sensitive data, as well as the use of IoT in certain industries, IoT standards, and the significance of individual protocols. These days, SG cybersecurity is essential to ensuring the global security of our energy networks. Presenting a thorough overview of the security issues, risks, and potential solutions for Internet of Things-based SGs is the aim of this paper. Our research focuses on network vulnerabilities, security needs, and cyber-attacks in SG applications to assess their effects on the network and provide guidance for future paths in cyber-security development.
IoT, artificial intelligence (AI), smart grids, data analytics, operational efficiency, cybersecurity.
Copyright (c) 2024 Alireza Zabihi, Mohammad Parhamfar, Maryam Khodadadi (Author) https://creativecommons.org/licenses/by-nc/4.0
Enhancing Cyber Attack Detection in Microgrids for Resilient Energy Networks
The threats posed by cyber-attacks on energy networks, especially microgrids, have become a significant concern as the global energy landscape becomes more interconnected and reliant on technological advancements. This study investigates the fundamental vulnerabilities in energy networks and the potential impact of cyber-attacks on power generation, consumption, and backup systems. The importance of robust identification systems cannot be overstated; real-time identification, mitigation, and response to cyber-attacks rely on advanced detection technology. This research underscores the critical role of active security protocols in safeguarding energy networks from cyber-attacks, with a focus on coding in MATLAB and the development of best practices. The paper explores two scenarios - normal operations and attack situations - for three key units: power generation, power consumption, and power storage. The methodology involves: primary detection through statistical analysis and secondary detection through SPC. These findings emphasize the essential role of strong cybersecurity measures in ensuring the stability and dependability of microgrids, particularly in the face of escalating cyber threats.
Copyright (c) 2024 Mohammad Parhamfar (Author) https://creativecommons.org/licenses/by-nc/4.0
A Light Weight Mobile Net SSD Algorithm based dentification and Detection of Multiple Defects in Ceramic Insulators
Presently, transmission lines play a major role for transferring electricity from generating stations to consumer premises. As the components are exposed to environmental conditions, damage to these components is more severe. Among those components insulators area accountable for 81.3% of line interruptions. In this paper, a drone-based transmission line inspection using mobile net single shot detection (SSD) is proposed that aims to detect the potential defects of insulators. The proposed system employs a regression-based algorithm using mobile net uses an upgraded compact mobile net SSD to identify targets network, allowing for precise categorization and placement of electrical equipment. Simulation is performed by loading the datasets that are collected. There are totally 800 images of ceramic insulators these are sub divided into 3 classes train class has images with 590 test class with 130 and validation (Val) class with 80. In this methodology, mobile net SSD can identify and detect the ceramic insulators with pollution flashover, corroded and broken. Precision, recall, F1 Score are calculated. Analysis is carried out on the variation of batch parameters, size etc to ascertain the effectiveness of the proposed method.
Copyright (c) 2024 P. Bhavya Sree, K. Balaji, Naveen Banoth, Mohammad Parhamfar (Author) https://creativecommons.org/licenses/by-nc/4.0
Battery Models and Estimation Techniques for Energy Storage Systems in Residential Buildings
This paper presents a comprehensive analysis of battery models and estimation techniques, focusing on their applications in residential buildings. The paper begins by exploring various battery models, including electrochemical, electrical equivalent circuit models. Different estimation techniques through various tests are discussed to provide insights into accurate battery parameter estimation. The applications of battery models in residential buildings are then discussed, highlighting energy storage sizing, energy management and optimization, and backup power supply. Additionally, it emphasizes the significance of software tools for battery modelling and simulation. Overall, this paper serves as a valuable resource for researchers, engineers, and decision-makers involved in the design and operation of residential energy systems, enabling them to make informed decisions regarding battery selection, sizing, and control strategies.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Emulation of Wind Turbine System using fuzzy controller- based vector Controlled Induction Motor Drive
This project presents a new approach for simulating wind turbines by using a Vector Controlled Induction Motor Drive (VCIMD) system enhanced with a Fuzzy Logic Controller (FLC). The goal of this system is to optimize the performance and efficiency of wind energy conversion. By accurately controlling the induction motor drive through vector control, the dynamic characteristics of wind turbines can be realistically emulated, allowing for a more true-to-life representation of real-world conditions. Integrating a Fuzzy Logic Controller provides intelligent adaptive control capabilities to improve stability and response. This synergistic combination of VCIMD and FLC not only enables efficient power generation but also ensures robust operation across varying wind conditions. The project investigates the dynamic interaction between these technologies, providing a comprehensive solution for wind turbine emulation that shows great potential for advancing renewable energy research and development. The findings from this study contribute to ongoing efforts to optimize wind energy systems for greater reliability and sustainability.
MPPT For Hybrid Energy System Using Machine Learning Techniques
The share of renewable energy sources (RES) in the total energy production has been growing rapidly. With the improvement in photovoltaic panel design and manufacturing techniques the per unit cost of photovoltaic systems (PVS) generated electricity has dropped considerably. Wind Energy Conversion Systems (WECS) have also seen a steady drop in the per unit electricity generation cost. This along with the environmental benefits of RES has made them a promising technology for power generation soon. To maximize the energy yield we need to operate the PVS and WECS at the maximum power point (MPP). This ensures that at any given moment we extract the maximum possible power from the systems. The PVS and WECS can be combined together to reduce the uncertainty around the availability of any given natural resource (solar or wind) at the given time. This Hybrid System can produce more power per square meter of land and is more reliable than the separate PVS or WECS. In this paper we discuss a machine learning (ML) based approach for faster maximum power point tracking (MPPT) of a hybrid energy system (HES) and its comparison with the conventional Perturb and Observe(P&amp;O) method.
Deciphering Blood Cells - Method for Blood Cell Analysis using Microscopic Images
Blood cell analysis of RBC and WBC from smear images is crucial for identifying disorders caused by abnormal blood cells, such as leukemia, anemia, frequent infections, and other diseases, including cancer. Pathologists find it challenging to distinguish between abnormal and normal cells in smear images. Manually counting each element is tedious and prone to human errors. Image processing techniques are used to enhance the smear image quality before segmentation. Identifying and segmenting smear images remains a challenge for pathologists. In this research, we propose an approach for identifying and segmenting smear images, followed by classifying normal and abnormal cells. This work implements Mask CNN for segmentation and two different algorithms: EfficientNet-B3 and DenseNet-121 for classification. The Mask CNN model&#39;s results for identification and segmentation are evaluated using mAP and IOU. For RBC, the best mAP value is 0.52 and IOU is 0.45. For WBC, the best mAP value is 0.56 and IOU is 0.52. Classification results outperform previous studies, with experiments on six different classes based on RBC and WBC cell normality and abnormality. The average overall accuracy for EfficientNet-B3 is 95% and DenseNet-121 is 97%, respectively.
Efficient Energy Management: Practical Tips for Household Electricity Conservation
This article outlines strategies for conserving electricity in residential settings. The focus is on simple yet impactful actions that homeowners can take to reduce energy consumption and lower utility bills. Suggestions include transitioning to LED (Light Emitting Diode) lighting, unplugging electronics when not in use, and investing in energy-efficient appliances bearing the Energy Star label. Optimizing HVAC (heating, ventilation, and air conditioning) systems through regular maintenance and utilizing programmable thermostats also proves beneficial. By implementing these measures, households can contribute to both financial savings and environmental sustainability.
Copyright (c) 2024 G. V. Mrudul, Rohit. G, Harshavardhan. G, Dhanush. K, Anudeep. B (Author) https://creativecommons.org/licenses/by-nc/4.0